code: def test_prediction_type(self):
      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(
              time_step=self.default_valid_timestep,
              prediction_type=prediction_type,
          )
  
imports: import tempfile
  from typing import Dict, List, Tuple
  import torch
  from diffusers import LCMScheduler
  from diffusers.utils.testing_utils import torch_device
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 6 for calling the LCMSchedulerTest obj's underlying member method code with example usage: obj.test_prediction_type() without return types